Handheld electronic device providing a learning function to facilitate correction of erroneous text entry in environment of text requiring multiple sequential actuations of the same key, and associated method

ABSTRACT

A handheld electronic device includes a reduced QWERTY keyboard and is enabled with disambiguation software that is operable to disambiguate text input. In addition to identifying and outputting representations of language objects that are stored in the memory and that correspond with a text input, the device provides a learning function which facilitates providing proposed corrected output by the device in certain circumstances of erroneous input.

BACKGROUND

1. Field

The disclosed and claimed concept relates generally to handheldelectronic devices and, more particularly, to a handheld electronicdevice having a reduced keyboard and a text input disambiguationfunction that can provide corrective proposed input during erroneoustext entry.

2. Background Information

Numerous types of handheld electronic devices are known. Examples ofsuch handheld electronic devices include, for instance, personal dataassistants (PDAs), handheld computers, two-way pagers, cellulartelephones, and the like. Many handheld electronic devices also featurewireless communication capability, although many such handheldelectronic devices are stand-alone devices that are functional withoutcommunication with other devices.

Such handheld electronic devices are generally intended to be portable,and thus are of a relatively compact configuration in which keys andother input structures often perform multiple functions under certaincircumstances or may otherwise have multiple aspects or featuresassigned thereto. With advances in technology, handheld electronicdevices are built to have progressively smaller form factors yet haveprogressively greater numbers of applications and features residentthereon. As a practical matter, the keys of a keypad can only be reducedto a certain small size before the keys become relatively unusable. Inorder to enable text entry, however, a keypad must be capable ofentering all twenty-six letters of the Latin alphabet, for instance, aswell as appropriate punctuation and other symbols.

One way of providing numerous letters in a small space has been toprovide a “reduced keyboard” in which multiple letters, symbols, and/ordigits, and the like, are assigned to any given key. For example, atouch-tone telephone includes a reduced keypad by providing twelve keys,of which ten have digits thereon, and of these ten keys eight have Latinletters assigned thereto. For instance, one of the keys includes thedigit “2” as well as the letters “A”, “B”, and “C”. Other known reducedkeyboards have included other arrangements of keys, letters, symbols,digits, and the like. Since a single actuation of such a key potentiallycould be intended by the user to refer to any of the letters “A”, “B”,and “C”, and potentially could also be intended to refer to the digit“2”, the input generally is an ambiguous input and is in need of sometype of disambiguation in order to be useful for text entry purposes.

In order to enable a user to make use of the multiple letters, digits,and the like on any given key, numerous keystroke interpretation systemshave been provided. For instance, a “multi-tap” system allows a user tosubstantially unambiguously specify a particular character on a key bypressing the same key a number of times equivalent to the position ofthe desired character on the key. Another exemplary keystrokeinterpretation system would include key chording, of which various typesexist. For instance, a particular character can be entered by pressingtwo keys in succession or by pressing and holding first key whilepressing a second key. Still another exemplary keystroke interpretationsystem would be a “press-and-hold/press-and-release” interpretationfunction in which a given key provides a first result if the key ispressed and immediately released, and provides a second result if thekey is pressed and held for a short period of time. Another keystrokeinterpretation system that has been employed is a software-based textdisambiguation function. In such a system, a user typically presses keysto which one or more characters have been assigned, generally pressingeach key one time for each desired letter, and the disambiguationsoftware attempt to predict the intended input.

Numerous such systems have been proposed, and while many have beengenerally effective for their intended purposes, such systems have notbeen without limitation. For instance, erroneous keying during textinput on a device employing text disambiguation can result in outputthat bears no similarity to the input intended by the user.

It would be desirable to provide an improved handheld electronic devicewith a reduced keyboard that seeks to mimic a QWERTY keyboard experienceor other particular keyboard experience. Such an improved handheldelectronic device might also desirably be configured with enoughfeatures to enable text entry and other tasks with relative ease.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the disclosed and claimed concept can be gainedfrom the following Description when read in conjunction with theaccompanying drawings in which:

FIG. 1 is a top plan view of an improved handheld electronic device inaccordance with the disclosed and claimed concept;

FIG. 2 is a schematic depiction of the improved handheld electronicdevice of FIG. 1;

FIG. 2A is a schematic depiction of a portion of the handheld electronicdevice of FIG. 2;

FIGS. 3A, 3B, and 3C are an exemplary flowchart depicting certainaspects of a disambiguation function that can be executed on thehandheld electronic device of FIG. 1;

FIG. 4 is another exemplary flowchart depicting certain aspects of alearning method that can be executed on the handheld electronic device;

FIG. 5 is an exemplary output during a text entry operation;

FIG. 6 is another exemplary output during another part of the text entryoperation;

FIG. 7 is another exemplary output during another part of the text entryoperation;

FIG. 8 is another exemplary output during another part of the text entryoperation;

FIG. 9 is a representation of an exemplary data table stored in a memoryof the handheld electronic device.

Similar numerals refer to similar parts throughout the specification.

DESCRIPTION

An improved handheld electronic device 4 is indicated generally in FIG.1 and is depicted schematically in FIG. 2. The exemplary handheldelectronic device 4 includes a housing 6 upon which are disposed aprocessor unit that includes an input apparatus 8, an output apparatus12, a processor 16, a memory 20, and at least a first routine. Theprocessor 16 may be, for instance, and without limitation, amicroprocessor (μP) and is responsive to inputs from the input apparatus8 and provides output signals to the output apparatus 12. The processor16 also interfaces with the memory 20. The processor 16 and the memory20 together form a processor apparatus. Examples of handheld electronicdevices are included in U.S. Pat. Nos. 6,452,588 and 6,489,950, whichare incorporated by record herein.

As can be understood from FIG. 1, the input apparatus 8 includes akeypad 24 and a thumbwheel 32. As will be described in greater detailbelow, the keypad 24 is in the exemplary form of a reduced QWERTYkeyboard including a plurality of keys 28 that serve as input members.It is noted, however, that the keypad 24 may be of other configurations,such as an AZERTY keyboard, a QWERTZ keyboard, or other keyboardarrangement, whether presently known or unknown, and either reduced ornot reduced. As employed herein, the expression “reduced” and variationsthereof in the context of a keyboard, a keypad, or other arrangement ofinput members, shall refer broadly to an arrangement in which at leastone of the input members has assigned thereto a plurality of linguisticelements such as, for example, characters in the set of Latin letters,whereby an actuation of the at least one of the input members, withoutanother input in combination therewith, is an ambiguous input since itcould refer to more than one of the plurality of linguistic elementsassigned thereto. As employed herein, the expression “linguisticelement” and variations thereof shall refer broadly to any element thatitself can be a language object or from which a language object can beconstructed, identified, or otherwise obtained, and thus would include,for example and without limitation, characters, letters, strokes,ideograms, phonemes, morphemes, metaphones, digits, and the like. Asemployed herein, the expression “language object” and variations thereofshall refer broadly to any type of object that may be constructed,identified, or otherwise obtained from one or more linguistic elements,that can be used alone or in combination to generate text, and thatwould include, for example and without limitation, words, shortcuts,symbols, ideograms, and the like.

The system architecture of the handheld electronic device 4advantageously is organized to be operable independent of the specificlayout of the keypad 24. Accordingly, the system architecture of thehandheld electronic device 4 can be employed in conjunction withvirtually any keypad layout substantially without requiring anymeaningful change in the system architecture. It is further noted thatcertain of the features set forth herein are usable on either or both ofa reduced keyboard and a non-reduced keyboard.

The keys 28 are disposed on a front face of the housing 6, and thethumbwheel 32 is disposed at a side of the housing 6. The thumbwheel 32can serve as another input member and is both rotatable, as is indicatedby the arrow 34, to provide selection inputs to the processor 16, andalso can be pressed in a direction generally toward the housing 6, as isindicated by the arrow 38, to provide another selection input to theprocessor 16.

As can further be seen in FIG. 1, many of the keys 28 include a numberof linguistic elements 48 disposed thereon. As employed herein, theexpression “a number of” and variations thereof shall refer broadly toany quantity, including a quantity of one. In the exemplary depiction ofthe keypad 24, many of the keys 28 include two linguistic elements, suchas including a first linguistic element 52 and a second linguisticelement 56 assigned thereto.

One of the keys 28 of the keypad 24 includes as the characters 48thereof the letters “Q” and “W”, and an adjacent key 28 includes as thecharacters 48 thereof the letters “E” and “R”. It can be seen that thearrangement of the characters 48 on the keys 28 of the keypad 24 isgenerally of a QWERTY arrangement, albeit with many of the keys 28including two of the characters 48.

The output apparatus 12 includes a display 60 upon which can be providedan output 64. An exemplary output 64 is depicted on the display 60 inFIG. 1. The output 64 includes a text component 68 and a variantcomponent 72. The variant component 72 includes a default portion 76 anda variant portion 80. The display also includes a caret 84 that depictsgenerally where the next input from the input apparatus 8 will bereceived.

The text component 68 of the output 64 provides a depiction of thedefault portion 76 of the output 64 at a location on the display 60where the text is being input. The variant component 72 is disposedgenerally in the vicinity of the text component 68 and provides, inaddition to the default proposed output 76, a depiction of the variousalternate text choices, i.e., alternates to the default proposed output76, that are proposed by an input disambiguation function in response toan input sequence of key actuations of the keys 28.

As will be described in greater detail below, the default portion 76 isproposed by the disambiguation function as being the most likelydisambiguated interpretation of the ambiguous input provided by theuser. The variant portion 80 includes a predetermined quantity ofalternate proposed interpretations of the same ambiguous input fromwhich the user can select, if desired. It is noted that the exemplaryvariant portion 80 is depicted herein as extending vertically below thedefault portion 76, but it is understood that numerous otherarrangements could be provided.

The memory 20 is depicted schematically in FIG. 2A. The memory 20 can beany of a variety of types of internal and/or external storage media suchas, without limitation, RAM, ROM, EPROM(s), EEPROM(s), and the like thatprovide a storage register for data storage such as in the fashion of aninternal storage area of a computer, and can be volatile memory ornonvolatile memory. The memory 20 additionally includes a number ofroutines depicted generally with the numeral 22 for the processing ofdata. The routines 22 can be in any of a variety of forms such as,without limitation, software, firmware, and the like. As will beexplained in greater detail below, the routines 22 include theaforementioned disambiguation function as an application, as well asother routines.

As can be understood from FIG. 2A, the memory 20 additionally includesdata stored and/or organized in a number of tables, sets, lists, and/orotherwise. Specifically, the memory 20 includes a generic word list 88,a new words database 92, one or more other data source 99, and a wordframe table 49.

Stored within the various areas of the memory 20 are a number oflanguage objects 100 and frequency objects 104. The language objects 100generally are each associated with an associated frequency object 104.The language objects 100 include, in the present exemplary embodiment, aplurality of word objects 108 and a plurality of N-gram objects 112. Theword objects 108 are generally representative of complete words withinthe language or are custom words stored in the memory 20. For instance,if the language stored in the memory 20 is, for example, English,generally each word object 108 would represent a word in the Englishlanguage or would represent a custom word.

Associated with substantially each word object 108 is a frequency object104 having frequency value that is indicative of the relative frequencywithin the relevant language of the given word represented by the wordobject 108. In this regard, the generic word list 88 includes aplurality of word objects 108 and associated frequency objects 104 thattogether are representative of a wide variety of words and theirrelative frequency within a given vernacular of, for instance, a givenlanguage. The generic word list 88 can be derived in any of a widevariety of fashions, such as by analyzing numerous texts and otherlanguage sources to determine the various words within the languagesources as well as their relative probabilities, i.e., relativefrequencies, of occurrences of the various words within the languagesources.

The N-gram objects 112 stored within the generic word list 88 are shortstrings of characters within the relevant language typically, forexample, one to three characters in length, and typically represent wordfragments within the relevant language, although certain of the N-gramobjects 112 additionally can themselves be words. However, to the extentthat an N-gram object 112 also is a word within the relevant language,the same word likely would be separately stored as a word object 108within the generic word list 88. As employed herein, the expression“string” and variations thereof shall refer broadly to an object havingone or more characters or components, and can refer to any of a completeword, a fragment of a word, a custom word or expression, and the like.

In the present exemplary embodiment of the handheld electronic device 4,the N-gram objects 112 include 1-gram objects, i.e., string objects thatare one character in length, 2-gram objects, i.e., string objects thatare two characters in length, and 3-gram objects, i.e., string objectsthat are three characters in length, all of which are collectivelyreferred to as N-grams 112. Substantially each N-gram object 112 in thegeneric word list 88 is similarly associated with an associatedfrequency object 104 stored within the generic word list 88, but thefrequency object 104 associated with a given N-gram object 112 has afrequency value that indicates the relative probability that thecharacter string represented by the particular N-gram object 112 existsat any location within any word of the relevant language. The N-gramobjects 112 and the associated frequency objects 104 are a part of thecorpus of the generic word list 88 and are obtained in a fashion similarto the way in which the word object 108 and the associated frequencyobjects 104 are obtained, although the analysis performed in obtainingthe N-gram objects 112 will be slightly different because it willinvolve analysis of the various character strings within the variouswords instead of relying primarily on the relative occurrence of a givenword.

The present exemplary embodiment of the handheld electronic device 4,with its exemplary language being the English language, includestwenty-six 1-gram N-gram objects 112, i.e., one 1-gram object for eachof the twenty-six letters in the Latin alphabet upon which the Englishlanguage is based, and further includes 676 2-gram N-gram objects 112,i.e., twenty-six squared, representing each two-letter permutation ofthe twenty-six letters within the Latin alphabet.

The N-gram objects 112 also include a certain quantity of 3-gram N-gramobjects 112, primarily those that have a relatively high frequencywithin the relevant language. The exemplary embodiment of the handheldelectronic device 4 includes fewer than all of the three-letterpermutations of the twenty-six letters of the Latin alphabet due toconsiderations of data storage size, and also because the 2-gram N-gramobjects 112 can already provide a meaningful amount of informationregarding the relevant language. As will be set forth in greater detailbelow, the N-gram objects 112 and their associated frequency objects 104provide frequency data that can be attributed to character strings forwhich a corresponding word object 108 cannot be identified or has notbeen identified, and typically is employed as a fallback data source,although this need not be exclusively the case.

In the present exemplary embodiment, the language objects 100 and thefrequency objects 104 are maintained substantially inviolate in thegeneric word list 88, meaning that the basic language dictionary remainssubstantially unaltered within the generic word list 88, and thelearning functions that are provided by the handheld electronic device 4and that are described below operate in conjunction with other objectthat are generally stored elsewhere in memory 20, such as, for example,in the new words database 92.

The new words database 92 stores additional word objects 108 andassociated frequency objects 104 in order to provide to a user acustomized experience in which words and the like that are usedrelatively more frequently by a user will be associated with relativelyhigher frequency values than might otherwise be reflected in the genericword list 88. More particularly, the new words database 92 includes wordobjects 108 that are user-defined and that generally are not found amongthe word objects 108 of the generic word list 88. Each word object 108in the new words database 92 has associated therewith an associatedfrequency object 104 that is also stored in the new words database 92.

The word frame table includes a plurality of word frames 51 and,associated with each word frame 51, one or more language objects 100. Aswill be described in greater detail below, each word frame 51 is arepresentation of each language object 100 that is associated therewith.The word frames 51 are advantageously configured to enable, in the eventof an erroneous text input, the outputting of at least a portion of anassociated language object 100 that is likely to be what the user hadintended to enter but which was incorrectly entered.

FIGS. 3A, 3B, and 3C depict in an exemplary fashion the generaloperation of certain aspects of the disambiguation function of thehandheld electronic device 4. Additional features, functions, and thelike are depicted and described elsewhere.

An input is detected, as at 204, and the input can be any type ofactuation or other operation as to any portion of the input apparatus 8.A typical input would include, for instance, an actuation of a key 28having a number of characters 48 thereon, or any other type of actuationor manipulation of the input apparatus 8.

The disambiguation function then determines, as at 212, whether thecurrent input is an operational input, such as a selection input, adelimiter input, a movement input, an alternation input, or, forinstance, any other input that does not constitute an actuation of a key28 having a number of characters 48 thereon. If the input is determinedat 212 to not be an operational input, processing continues at 216 byadding the input to the current input sequence which may or may notalready include an input.

Many of the inputs detected at 204 are employed in generating inputsequences as to which the disambiguation function will be executed. Aninput sequence is built up in each “session” with each actuation of akey 28 having a number of characters 48 thereon. Since an input sequencetypically will be made up of at least one actuation of a key 28 having aplurality of characters 48 thereon, the input sequence will beambiguous. When a word, for example, is completed the current session isended an a new session is initiated.

An input sequence is gradually built up on the handheld electronicdevice 4 with each successive actuation of a key 28 during any givensession. Specifically, once a delimiter input is detected during anygiven session, the session is terminated and a new session is initiated.Each input resulting from an actuation of one of the keys 28 having anumber of the characters 48 associated therewith is sequentially addedto the current input sequence. As the input sequence grows during agiven session, the disambiguation function generally is executed witheach actuation of a key 28, i.e., input, and as to the entire inputsequence. Stated otherwise, within a given session, the growing inputsequence is attempted to be disambiguated as a unit by thedisambiguation function with each successive actuation of the variouskeys 28.

Once a current input representing a most recent actuation of the one ofthe keys 28 having a number of the characters 48 assigned thereto hasbeen added to the current input sequence within the current session, asat 216 in FIG. 3A, the disambiguation function generates, as at 220,substantially all of the permutations of the characters 48 assigned tothe various keys 28 that were actuated in generating the input sequence.In this regard, the “permutations” refer to the various strings that canresult from the characters 48 of each actuated key 28 limited by theorder in which the keys 28 were actuated. The various permutations ofthe characters in the input sequence are employed as prefix objects.

For instance, if the current input sequence within the current sessionis the ambiguous input of the keys <AS> and <OP>, the variouspermutations of the first character 52 and the second character 56 ofeach of the two keys 28, when considered in the sequence in which thekeys 28 were actuated, would be “SO”, “SP”, “AP”, and “AO”, and each ofthese is a prefix object that is generated, as at 220, with respect tothe current input sequence. As will be explained in greater detailbelow, the disambiguation function seeks to identify for each prefixobject one of the word objects 108 for which the prefix object would bea prefix.

For each generated prefix object, the memory 20 is consulted, as at 224,to identify, if possible, for each prefix object one of the word objects108 in the memory 20 that corresponds with the prefix object, meaningthat the sequence of letters represented by the prefix object would beeither a prefix of the identified word object 108 or would besubstantially identical to the entirety of the word object 108. Furtherin this regard, the word object 108 that is sought to be identified isthe highest frequency word object 108. That is, the disambiguationfunction seeks to identify the word object 108 that corresponds with theprefix object and that also is associated with a frequency object 104having a relatively higher frequency value than any of the otherfrequency objects 104 associated with the other word objects 108 thatcorrespond with the prefix object. During this step, linguistic sourcesother than the word frame table 49 are consulted to identify such wordobjects 108.

It is noted in this regard that the word objects 108 in the generic wordlist 88 are generally organized in data tables that correspond with thefirst two letters of various words. For instance, the data tableassociated with the prefix “CO” would include all of the words such as“CODE”, “COIN”, “COMMUNICATION”, and the like. Depending upon thequantity of word objects 108 within any given data table, the data tablemay additionally include sub-data tables within which word objects 108are organized by prefixes that are three characters or more in length.Continuing onward with the foregoing example, if the “CO” data tableincluded, for instance, more than 256 word objects 108, the “CO” datatable would additionally include one or more sub-data tables of wordobjects 108 corresponding with the most frequently appearingthree-letter prefixes. By way of example, therefore, the “CO” data tablemay also include a “COM” sub-data table and a “CON” sub-data table. If asub-data table includes more than the predetermined number of wordobjects 108, for example a quantity of 256, the sub-data table mayinclude further sub-data tables, such as might be organized according toa four letter prefixes. It is noted that the aforementioned quantity of256 of the word objects 108 corresponds with the greatest numericalvalue that can be stored within one byte of the memory 20.

Accordingly, when, at 224, each prefix object is sought to be used toidentify a corresponding word object 108, and for instance the instantprefix object is “AP”, the “AP” data table will be consulted. Since allof the word objects 108 in the “AP” data table will correspond with theprefix object “AP”, the word object 108 in the “AP” data table withwhich is associated a frequency object 104 having a frequency valuerelatively higher than any of the other frequency objects 104 in the“AP” data table is identified. The identified word object 108 and theassociated frequency object 104 are then stored in a result registerthat serves as a result of the various comparisons of the generatedprefix objects with the contents of the memory 20.

It is noted that one or more, or possibly all, of the prefix objectswill be prefix objects for which a corresponding word object 108 is notidentified in the memory 20. Such prefix objects are considered to beorphan prefix objects and are separately stored or are otherwiseretained for possible future use. In this regard, it is noted that manyor all of the prefix objects can become orphan object if, for instance,the user is trying to enter a new word or, for example, if the user hasmis-keyed and no word corresponds with the mis-keyed input.

Processing continues, as at 232, where duplicate word objects 108associated with relatively lower frequency values are deleted from theresult. Such a duplicate word object 108 could be generated, forinstance, by one of the other data sources 99.

Once the duplicate word objects 108 and the associated frequency objects104 have been removed at 232, processing continues to 236 wherein theremaining prefix objects are arranged in an output set in decreasingorder of frequency value.

If it is determined, as at 240, that the flag has been set, meaning thata user has made a selection input, either through an express selectioninput or through an alternation input of a movement input, then thedefault output 76 is considered to be “locked,” meaning that theselected variant will be the default prefix until the end of thesession. If it is determined at 240 that the flag has been set, theprocessing will proceed to 241 where the contents of the output set willbe altered, if needed, to provide as the default output 76 an outputthat includes the selected prefix object, whether it corresponds with aword object 108 or is an artificial variant. In this regard, it isunderstood that the flag can be set additional times during a session,in which case the selected prefix associated with resetting of the flagthereafter becomes the “locked” default output 76 until the end of thesession or until another selection input is detected.

If it was determined at 240 that the flag was not set, or alternativelyafter alteration of the output set has been completed at 241, processingcontinues at 242 where it is determined whether or not any word frame 51corresponds with the input. As mentioned elsewhere herein, the wordframes 51 are stored in the word frame table 49. The word frames 51advantageously are configured to be recognizable by the disambiguationsystem 22 and, in the event of certain types of erroneous input, toenable the outputting of at least a portion of a language object 100 asa proposed corrected text input.

The exemplary word frame table 49 is depicted in greater detail in FIG.9. The language objects 100 that are depicted as being stored thereincan be stored as duplicate entries of language objects 100 storedelsewhere in the memory 20. Alternatively, the language objects 100depicted in FIG. 9 could actually each be in the form of a pointer toanother location in the memory 20, such as to a location in the genericword list 88, the new words database 92, and/or one or more of the otherdata sources 99, where a language object 100 is stored.

The word frames 51 are configured to be employed, as one example, in asituation of an intended input by a user of a text item such as a wordwhich, if input correctly, would require a sequential plurality ofactuations of the same key. Such a situation would occur, for instance,if the user was attempting to enter the word “connect”, wherein correctentry of the word would require a plurality of actuations of the key towhich the character “N” is assigned in order to enter the “nn” portionof “connect”. In the context of the exemplary reduced keyboard of thehandheld electronic device 4, a similar situation would occur if theuser was attempting to enter the word “regret”, wherein the characters48 “E” and “R” are each assigned to the same key 28, i.e., to the <ER>key 28.

Such words, in the context of the particular layouts of the characters48 of the keypads 24 on which the words are being entered, are prone toerroneous entry due to, in accordance with the instant example, acorrect entry thereof requiring a sequential plurality of actuations ofthe same key. For instance, if two sequential actuations of a particularkey are required to correctly enter a word, it is possible that the usermay erroneously actuate the particular key only one time, or the usermay erroneously actuate the particular key three or more times. Ineither event, the output on the display 60 is erroneous. In thesituation of a reduced keyboard, such as the keypad 24, a mis-keying canresult in an output that bears little if any resemblance to the textthat was desired to be input by the user. The improved method and device4 of the disclosed and claimed concept can also be employed in asituation where a user intends to enter a certain key sequence andmistakenly actuates a key 28 of the input a plurality of times when thekey 28 should have only been actuated once, and the incorrect inputitself corresponds with a language object 100 in the memory 20.

Advantageously, therefore, each language object 100 that comprises asequential plurality of linguistic elements that are each assigned tothe same key 28 of the keypad 24 is associated with a particular wordframe 51. The particular word frame 51 is configured to berepresentative of the associated language object 100. Specifically, theword frame 51 is configured to comprise a contracted portion 55 that isrepresentative of the aforementioned sequential plurality of linguisticelements of the language object 100 that are each assigned to the samekey 28. For example, and as is depicted generally in FIG. 9, thelanguage object 100 corresponding with the word “connect” would beassociated with a word frame 51 that comprises as a contracted portion55 thereof a representation of a single actuation of the <BN> key 28.The contracted portion 55 would be representative of any sequentialquantity of the linguistic elements assigned to the <BN> key 28, i.e.,would correspond with any sequential quantity of actuations of the <BN>key 28.

The word frame 51 may additionally include a root portion 57 that isrepresentative of the linguistic elements of the associated languageobject 100 that are not part of a sequential plurality of linguisticelements that are each assigned to one key 28. In the instant example,therefore, the word frame 51 that has associated therewith the languageobject 100 “connect” could be characterized as “CO<BN>ECT”, with thecontracted portion 55 being the aforementioned <BN>, and with the rootportion 57 comprising the characters “CO” preceding the contractedportion 55 and further comprising the characters “ECT” succeeding thecontracted portion 55. The exemplary root portion 57 is depicted as anumber of characters 48, and the contracted portion 55 is depicted as akey 28. It is noted, however, that the exemplary representation depictedherein is not intended to be limiting, and it is noted that thecontracted portion 55, as well as the root portion 57, could be storedor characterized as, for example, keystrokes, characters, metaphones,etc., without limitation.

The word frames 51 are, for the most part, derived in advance from thegeneric word list 88. That is, the language objects 100 in the genericword list 88 are analyzed to identify those language objects 100 whichwould be typed by actuating a given key 28 a sequential plurality oftimes. As a result, the contents of the word frame table 49 are highlydependent upon the layout of the characters 48 on the various keys 28 ofthe keypad 24. Upon identification of such a language object 100, and ifa corresponding word frame 51 that corresponds with the identifiedlanguage object 100 has not already been constructed, a word frame 51 isconstructed, and the identified language object 100 is associatedtherewith.

After all of the needed word frames 51 have been created, the genericword list 88 may again be analyzed to identify any word objects 100which correspond with a word frame 51 that but are not alreadyassociated therewith. In this regard, it can be seen from FIG. 9 that aplurality of language objects 100, i.e., those for the words “cat” and“cast”, are associated with the word frame 51 “C<AS>T”, with “<AS>”referring to one or more actuations of the <AS> key 28.

Such entries in the word frame table 49 likely would have been generatedby first identifying the language object 100 for the word “cast”, forinstance, which is typed from a series of keystrokes comprising twosequential actuations of the <AS> key 28, and with construction of thecorresponding word frame 51 “C<AS>T”. The language object 100 for theword “cast” would then be stored in the word frame table 49 as beingassociated with the word frame 51 “C<AS>T”, or would otherwise beassociated therewith. The generic word list 88 would then have beenre-analyzed, for instance, which would have resulted in identificationof the language object 100 for the word “cat” as corresponding with theword frame 51 “C<AS>T”. The language object 100 for the word “cat” wouldthen be stored in the word frame table 49 as being associated with theword frame 51 “C<AS>T”, or would otherwise be associated therewith.

In this regard, the language object 100 for the word “cat” would havebeen identified as corresponding with the word frame 51 “C<AS>T”, eventhough it can be seen that typing of the word “cat” does not require asequential plurality of strokes of a given key 28, such as the <AS> key28. However, it is anticipated that a user intending to type the word“cat” might accidentally actuate the <AS> key 28 twice instead ofactuating it only once, such as by inputting <CV><AS><AS><TY>, whichordinarily would result in outputting of the words “cast” and “vast”.The method and device 4 in accordance with the disclosed and claimedconcept advantageously will, as set forth in greater detail below,additionally output “cat” as a proposed disambiguation of the input.

It is noted, however, that additional word frames 51 can be learned andadded to the word frame table 49. For instance, a new language object100 is entered on the handheld electronic device 4 may additionally beanalyzed to determine whether it is typed using a key sequence thatcomprises a sequential plurality of actuations of the same key 28. Ifso, it is determined whether or not the new language object 100corresponds with a word frame 51 already in existence. If such a wordframe 51 already exists, the new language object 100 is added to theword frame table 49 or is otherwise associated with the word frame 51.

On the other hand, if no corresponding word frame 51 already exists, anew word frame 51 is created, and the new language object 100 is addedto the word frame table 49 or is otherwise associated with the new wordframe 51. Additionally, the generic word list 88 and any otherlinguistic sources may be analyzed to determine whether or not any otherlanguage object 100 corresponds with the new word frame 51. Any suchother language object 100 that is identified is added to the word frametable 49 or is otherwise associated with the new word frame 51.

New language objects 100 can be entered on the handheld electronicdevice 4 in any of a variety of ways. For instance, a new languageobject 100 can be entered on the handheld electronic device 4 as aninput comprising a number of key actuations. A new language object 100could also be entered on the handheld electronic device 4 as a result ofbeing received on the handheld electronic device 4. For instance, thehandheld electronic device 4 can receive new language objects 100 viareceipt of electronic mail or via operation of other messaging routines.Moreover, new language objects 100 can be received on the handheldelectronic device 4 as a result of installation of new applications orroutines. New language objects 100 can be received or otherwise enteredon the handheld electronic device 4 in other ways as well.

During text entry, and as mentioned above, the disambiguation routinegenerates prefixes, as at 220, and seeks language objects 100, as at224. All such identified language objects 100 will correspond directlywith the input. However, in order to provide proposed corrected outputin the event of a mis-keying of a sequential plurality of actuations ofthe same key, the input is compared, as at 242 in FIG. 3C, with the wordframes 51 in the word frame table 49. Specifically, the input iscompared with each of a number of the word frames 51 to determine if anyword frame 51 corresponds with the input. For instance, if the input wasan ambiguous input of actuations of the keys 28 <CV><OP><BN><ER>, itwould be determined whether or not the input comprised one or moresequential actuations of, in the present example, the <BN> key 28. Itwould also be determined whether or not another portion of the inputcorresponded with at least some of the linguistic elements of the rootportion 57 of the same word frame 51. In the example of the word frame51 “CO<BN>ECT” and the exemplary input <CV><OP><BN><ER>, it would bedetermined whether or not the input comprised key actuations whichpreceded and followed the actuation(s) of the <BN> key 28 and thatcorresponded with, for example, the characters 48 “C”, “ ”, and “E” ofthe root portion 57.

In the present example, an input comprising actuations of the keys 28<CV><OP><BN><ER> would cause the word frame 51 “CO<BN>ECT” to beidentified in the affirmative at 242. This is because the first two keyactuations <CV><OP> would correspond with the tow sequential linguisticelements “C” and “O” of the root portion 57 that precede the contractedportion 55, the actuation of the <BN> key 28 would correspond with thecontracted portion 55, and the actuation of the <ER> key 28 wouldcorrespond with the character “E” of the root portion 57 thatimmediately follows the contracted portion 55.

Processing thereafter would proceed to 243 where it would be determinedwhether or not each language object 100 which corresponds with theidentified word frame 51 is included in the output set which wasgenerated at 236 and 241. In the present example, the language object100 “connect” is associated with the word frame 51 “CO<BN>ECT”. If it isdetermined that the language object 100 “connect” is not reflected inthe output set, and based upon the method set forth above at theelements 220 and 224 it can be assumed that the input <CV><OP><BN><ER>would not result in identification of the language object 100 “connect”in any of the generic word list 88, the new words database 92, and theother data sources 99, the language object 100 “connect” would, at 244,be added at least in part to the output set.

Processing would thereafter proceed to 245 where at least a portion ofthe output set would be output, for instance, on the display 60.Processing would thereafter proceed, as at 246, to the numeral 204 inFIG. 3A.

On the other hand, if it is determined at 243 that the language object100 that is associated with the identified word frame 51 is, in fact,already included in the output set, processing would proceed directly to245 where at least a portion of the output set would be output withoutadding the additional language object 100. Processing would thereafterproceed, as at 246, to the numeral 204 in FIG. 3A. This would be thesituation if, for example, the input was <CV><OP><BN><BN><ER>. Such asinput would result in the language object 100 “connect” being identifiedat 224 in the generic word list 88 and being included in the output set.However, if the desired input “connect” was mis-keyed as<CV><OP><BN><ER> as in the present mis-keying example, the languageobject 100 “connect” would not have been identified at 224 in thegeneric word list 88, but advantageously would have been added to theoutput set at 244 as a result of the word frame 51 “CO<BN>ECT” beingidentified at 242 as corresponding with the mis-keyed input.Advantageously, therefore, the improved method of FIG. 3C enablesoutputting of certain words which the user may have intended to type butwhich were mis-keyed by the user.

If it is determined at 242 that no word frame 51 corresponds with theinput, it is then determined, as at 247, whether any word frames 51 havea root portion 57 that is within an edit distance equal to a value ofone with respect to a similar portion of the input. In this regard, anedit distance of one between a portion of the input and a root portion57 would exist, for instance, in any of the following situations: i) allbut one of the key actuations of that portion of the input correspondwith similar portions of the root portion 57, with one of the keyactuations not corresponding with one of the linguistic elements of theroot portion 57; ii) all of the key actuations of that portion of theinput correspond with similar portions of the root portion 57, but theroot portion 57 has one linguistic element more than that portion of theinput; and iii) the root portion 57 corresponds with similar portions ofthe input, but the input has one linguistic element more than that partof the root portion 57.

If one of these three situations occurred in conjunction withcorrespondence between the input and the contracted portion 55 of theword frame 51, there would be a partial correspondence between the inputand the identified word frame 51, i.e., correspondence within an editdistance of one. In the event of such partial correspondence, processingwould continue, as at 248, where it would be determined whether thesearching at 224 identified any language objects 110. If languageobjects 110 were identified at 224, processing would, in the presentexemplary embodiment, continue to 245 where the output set would beoutput at least in part without the addition thereto of a languageobject that was associated with the at least partially correspondingword frame 51 identified at 247.

On the other hand, if it is determined at 248 that no language objects100 were identified at 224, processing would continue at 244 where thelanguage objects 100 associated with the word frame 51 identified at 247would be added to the output set. Processing would thereafter proceed to245 where at least a portion of the output set would be output, andthereafter processing would proceed, as at 246, to the numeral 204 inFIG. 3A.

In the present example of the input <CV><OP><BN><ER>, the disambiguationsystem would have identified at 224 the language object 100 for the word“cone”. Additionally, the system would at 244 add to the output set atleast a portion of the language object 100 “connect”. The output on thedisplay 60 in response to such an input would comprise “cone” as thedefault portion 76 and “conne” as the variant portion 80. That is, theoutput “conne” would comprise one character more than the number of keyactuations of the input. Some additional examples of mis-keyed attemptsto enter “connect” are presented below.

If the input was <CV><OP><BN><BN><BN><ER>, the system would again at 244add to the output set at least a portion of the language object 100“connect”. In response to such an input, the output on the display 60would comprise “conne” as the default portion 76. That is, the output“conne” would comprise one character fewer than the number of keyactuations of the input. It is noted that in this example and in theexample of the preceding paragraph, the input corresponded with both thecontracted portion 55 and the root portion 57 of the word frame“CO<BN>ECT”.

If the input was <CV><BN>, the disambiguation system might haveidentified at 224 the language object 100 for the word “cnidoblast” andthus would output “cn” as the default portion 76. If the next keystrokeof the input is <BN>, thus making the current input <CV><BN><BN>, nocorresponding language object 100 would have been identified at 224.Moreover, no word frame 51 would have been identified at 242. However,at 247 the word frame 51 “CO<BN>ECT” would be identified as being withinan edit distance of one to the input. That is, the <BN><BN> portion ofthe input would correspond with the contracted portion 55, and the <CV>portion of the input would meet situation ii) above, i.e., the keyactuation <CV> corresponds with the character “C” of the root portion57, but the root portion 57 additionally has the character “O”, which isone linguistic element more than that portion of the input. This meetsthe requirement of partial correspondence at 247. Since the input<CV><BN><BN> would not result in identification at 224 of any languageobject 100, the language object 100 “connect”, which is associated withthe word frame 51 “CO<BN>ECT” identified at 247, would be added at 244to the output set, and at 245 “conn” would be output as the defaultportion 76. The output again would have one character more than theinput, but this time would be a result of only partial correspondencebetween the input and the word frame 51.

If the input was <CV><TY><BN><BN>, no language object 100 would beidentified at 224. Moreover, no word frame 51 would have been identifiedat 242. However, at 247 the word frame 51 “CO<BN>ECT” would beidentified as being within an edit distance of one to the input. Thatis, the <BN><BN> portion of the input would correspond with thecontracted portion 55, and the <CV><TY> portion of the input would meetsituation i) above, i.e., the key actuation <CV> corresponds with thecharacter “C” of the root portion 57, but <TY> does not correspond withthe character “O” of the root portion 57. This meets the requirement ofpartial correspondence at 247. Since the input <CV><TY><BN><BN> wouldnot result in identification at 224 of any language object 100, thelanguage object 100 “connect”, which is associated with the word frame51 “CO<BN>ECT” identified at 247, would be added at 244 to the outputset, and at 245 “conn” would be output as the default portion 76.

It will be apparent that certain word frames 51 can have multiplecontracted portions 55, such as the way in which the word frame 51B<OP><JK><ER>P<ER>, which corresponds with the language object 100“bookkeeper”, has four contracted portions 55. Correspondence with sucha word frame 51 can occur in a fashion that will be apparent in view ofthe foregoing. It is further noted that the root portion 57 of certainword frames 51 can be split into multiple parts and can exist, forinstance, between multiple contracted portions 55, again such as withthe word frame 51 B<OP><JK><ER>P<ER>. Again, correspondence with such aword frame 51 can occur in a fashion that will be apparent in view ofthe foregoing.

If the detected input is determined, as at 212, to be an operationalinput, processing then continues to determine the specific nature of theoperational input. For instance, if it is determined, as at 252, thatthe current input is a selection input, processing continues at 254where the flag is set. Processing then returns to detection ofadditional inputs as at 204.

If it is determined, as at 260, that the input is a delimiter input,processing continues at 264 where the current session is terminated andprocessing is transferred, as at 266, to the learning functionsubsystem, as at 404 of FIG. 4. A delimiter input would include, forexample, the actuation of a <SPACE> key 116, which would both enter adelimiter symbol and would add a space at the end of the word, actuationof the <ENTER> key, which might similarly enter a delimiter input andenter a space, and by a translation of the thumbwheel 32, such as isindicated by the arrow 38, which might enter a delimiter input withoutadditionally entering a space.

It is first determined, as at 408, whether the default output at thetime of the detection of the delimiter input at 260 matches a wordobject 108 in the memory 20. If it does not, this means that the defaultoutput is a user-created output that should be added to the new wordsdatabase 92 for future use. In such a circumstance processing thenproceeds to 412 where the default output is stored in the new wordsdatabase 92 as a new word object 108. Additionally, a frequency object104 is stored in the new words database 92 and is associated with theaforementioned new word object 108. The new frequency object 104 isgiven a relatively high frequency value, typically within the upperone-fourth or one-third of a predetermined range of possible frequencyvalues.

In this regard, frequency objects 104 are given an absolute frequencyvalue generally in the range of zero to 65,535. The maximum valuerepresents the largest number that can be stored within two bytes of thememory 20. The new frequency object 104 that is stored in the new wordsdatabase 92 is assigned an absolute frequency value within the upperone-fourth or one-third of this range, particularly since the new wordwas used by a user and is likely to be used again.

With further regard to frequency object 104, it is noted that within agiven data table, such as the “CO” data table mentioned above, theabsolute frequency value is stored only for the frequency object 104having the highest frequency value within the data table. All of theother frequency objects 104 in the same data table have frequency valuesstored as percentage values normalized to the aforementioned maximumabsolute frequency value. That is, after identification of the frequencyobject 104 having the highest frequency value within a given data table,all of the other frequency objects 104 in the same data table areassigned a percentage of the absolute maximum value, which representsthe ratio of the relatively smaller absolute frequency value of aparticular frequency object 104 to the absolute frequency value of theaforementioned highest value frequency object 104. Advantageously, suchpercentage values can be stored within a single byte of memory, thussaving storage space within the handheld electronic device 4.

Upon creation of the new word object 108 and the new frequency object104, and storage thereof within the new words database 92, processing istransferred to 420 where the learning process is terminated. Processingis then returned to the main process, as at 204. If at 408 it isdetermined that the word object 108 in the default output 76 matches aword object 108 within the memory 20, processing is returned directly tothe main process at 204.

With further regard to the identification of various word objects 108for correspondence with generated prefix objects, it is noted that thememory 20 can include a number of other data sources 99 in addition tothe generic word list 88, the new words database 92, and the word frametable, all of which can be considered linguistic sources. It isunderstood that the memory 20 might include any number of other datasources 99. The other data sources 99 might include, for example, anaddress database, a speed-text database, or any other data sourcewithout limitation. An exemplary speed-text database might include, forexample, sets of words or expressions or other data that are eachassociated with, for example, a character string that may beabbreviated. For example, a speed-text database might associate thestring “br” with the set of words “Best Regards”, with the intentionthat a user can type the string “br” and receive the output “BestRegards”.

In seeking to identify word objects 108 that correspond with a givenprefix object, the handheld electronic device 4 may poll all of the datasources in the memory 20. For instance the handheld electronic device 4may poll the generic word list 88, the new words database 92, and theother data sources 99 to identify word objects 108 that correspond withthe prefix object. The contents of the other data sources 99 may betreated as word objects 108, and the processor 16 may generate frequencyobjects 104 that will be associated with such word objects 108 and towhich may be assigned a frequency value in, for example, the upperone-third or one-fourth of the aforementioned frequency range. Assumingthat the assigned frequency value is sufficiently high, the string “br”,for example, would typically be output to the display 60. If a delimiterinput is detected with respect to the portion of the output having theassociation with the word object 108 in the speed-text database, forinstance “br”, the user would receive the output “Best Regards”, itbeing understood that the user might also have entered a selection inputas to the exemplary string “br”.

The contents of any of the other data sources 99 may be treated as wordobjects 108 and may be associated with generated frequency objects 104having the assigned frequency value in the aforementioned upper portionof the frequency range. After such word objects 108 are identified, thenew word learning function can, if appropriate, act upon such wordobjects 108 in the fashion set forth above.

If it is determined, such as at 268, that the current input is amovement input, such as would be employed when a user is seeking to editan object, either a completed word or a prefix object within the currentsession, the caret 84 is moved, as at 272, to the desired location, andthe flag is set, as at 276. Processing then returns to where additionalinputs can be detected, as at 204.

In this regard, it is understood that various types of movement inputscan be detected from the input device 8. For instance, a rotation of thethumbwheel 32, such as is indicated by the arrow 34 of FIG. 1, couldprovide a movement input. In the instance where such a movement input isdetected, such as in the circumstance of an editing input, the movementinput is additionally detected as a selection input. Accordingly, and asis the case with a selection input such as is detected at 252, theselected variant is effectively locked with respect to the defaultportion 76 of the output 64. Any default output 76 during the samesession will necessarily include the previously selected variant.

In the present exemplary embodiment of the handheld electronic device 4,if it is determined, as at 252, that the input is not a selection input,and it is determined, as at 260, that the input is not a delimiterinput, and it is further determined, as at 268, that the input is not amovement input, in the current exemplary embodiment of the handheldelectronic device 4 the only remaining operational input generally is adetection of the <DELETE> key 86 of the keys 28 of the keypad 24. Upondetection of the <DELETE> key 86, the final character of the defaultoutput is deleted, as at 280. Processing thereafter returns to 204 whereadditional input can be detected.

An exemplary input sequence is depicted in FIGS. 1 and 5-8. In thisexample, the user is attempting to enter the word “APPLOADER”, and thisword presently is not stored in the memory 20. In FIG. 1 the user hasalready typed the “AS” key 28. Since the data tables in the memory 20are organized according to two-letter prefixes, the contents of theoutput 64 upon the first keystroke are obtained from the N-gram objects112 within the memory. The first keystroke “AS” corresponds with a firstN-gram object 112 “S” and an associated frequency object 104, as well asanother N-gram object 112 “A” and an associated frequency object 104.While the frequency object 104 associated with “S” has a frequency valuegreater than that of the frequency object 104 associated with “A”, it isnoted that “A” is itself a complete word. A complete word is alwaysprovided as the default output 76 in favor of other prefix objects thatdo not match complete words, regardless of associated frequency value.As such, in FIG. 1, the default portion 76 of the output 64 is “A”.

In FIG. 5, the user has additionally entered the “OP” key 28. Thevariants are depicted in FIG. 5. Since the prefix object “SO” is also aword, it is provided as the default output 76. In FIG. 6, the user hasagain entered the “OP” key 28 and has also entered the “L” key 28. It isnoted that the exemplary “L” key 28 depicted herein includes only thesingle character 48 “L”.

It is assumed in the instant example that no operational inputs havethus far been detected. The default output 76 is “APPL”, such as wouldcorrespond with the word “APPLE”. The prefix “APPL” is depicted both inthe text component 68, as well as in the default portion 76 of thevariant component 72. Variant prefix objects in the variant portion 80include “APOL”, such as would correspond with the word “APOLOGIZE”, andthe prefix “SPOL”, such as would correspond with the word “SPOLIATION”.

It is particularly noted that the additional variants “AOOL”, “AOPL”,“SOPL”, and “SOOL” are also depicted as variants 80 in the variantcomponent 72. Since no word object 108 corresponds with these prefixobjects, the prefix objects are considered to be orphan prefix objectsfor which a corresponding word object 108 was not identified. In thisregard, it may be desirable for the variant component 72 to include aspecific quantity of entries, and in the case of the instant exemplaryembodiment the quantity is seven entries. Upon obtaining the result at224, if the quantity of prefix objects in the result is fewer than thepredetermined quantity, the disambiguation function will seek to provideadditional outputs until the predetermined number of outputs areprovided.

In FIG. 7 the user has additionally entered the “OP” key 28. In thiscircumstance, and as can be seen in FIG. 7, the default portion 76 ofthe output 64 has become the prefix object “APOLO” such as wouldcorrespond with the word “APOLOGIZE”, whereas immediately prior to thecurrent input the default portion 76 of the output 64 of FIG. 6 was“APPL” such as would correspond with the word “APPLE.” Again, assumingthat no operational inputs had been detected, the default prefix objectin FIG. 7 does not correspond with the previous default prefix object ofFIG. 6. As such, a first artificial variant “APOLP” is generated and inthe current example is given a preferred position. The aforementionedartificial variant “APOLP” is generated by deleting the final characterof the default prefix object “APOLO” and by supplying in its place anopposite character 48 of the key 28 which generated the final characterof the default portion 76 of the output 64, which in the current exampleof FIG. 7 is “P”, so that the aforementioned artificial variant is“APOLP”.

Furthermore, since the previous default output “APPL” corresponded witha word object 108, such as the word object 108 corresponding with theword “APPLE”, and since with the addition of the current input theprevious default output “APPL” no longer corresponds with a word object108, two additional artificial variants are generated. One artificialvariant is “APPLP” and the other artificial variant is “APPLO”, andthese correspond with the previous default output “APPL” plus thecharacters 48 of the key 28 that was actuated to generate the currentinput. These artificial variants are similarly output as part of thevariant portion 80 of the output 64.

As can be seen in FIG. 7, the default portion 76 of the output 64“APOLO” no longer seems to match what would be needed as a prefix for“APPLOADER”, and the user likely anticipates that the desired word“APPLOADER” is not already stored in the memory 20. As such, the userprovides a selection input, such as by scrolling with the thumbwheel 32until the variant string “APPLO” is highlighted. The user then continuestyping and enters the “AS” key.

The output 64 of such action is depicted in FIG. 8. Here, the string“APPLOA” is the default portion 76 of the output 64. Since the variantstring “APPLO” became the default portion 76 of the output 64 (notexpressly depicted herein) as a result of the selection input as to thevariant string “APPLO”, and since the variant string “APPLO” does notcorrespond with a word object 108, the character strings “APPLOA” and“APPLOS” were created as an artificial variants. Additionally, since theprevious default of FIG. 7, “APOLO” previously had corresponded with aword object 108, but now is no longer in correspondence with the defaultportion 76 of the output 64 of FIG. 8, the additional artificialvariants of “APOLOA” and “APOLOS” were also generated. Such artificialvariants are given a preferred position in favor of the three displayedorphan prefix objects.

Since the current input sequence in the example no longer correspondswith any word object 108, the portions of the method related toattempting to find corresponding word objects 108 are not executed withfurther inputs for the current session. That is, since no word object108 corresponds with the current input sequence, further inputs willlikewise not correspond with any word object 108. Avoiding the search ofthe memory 20 for such nonexistent word objects 108 saves time andavoids wasted processing effort.

As the user continues to type, the user ultimately will successfullyenter the word “APPLOADER” and will enter a delimiter input. Upondetection of the delimiter input after the entry of “APPLOADER”, thelearning function is initiated. Since the word “APPLOADER” does notcorrespond with a word object 108 in the memory 20, a new word object108 corresponding with “APPLOADER” is generated and is stored in the newwords database 92, along with a corresponding new frequency object 104which is given an absolute frequency in the upper, say, one-third orone-fourth of the possible frequency range. In this regard, it is notedthat the new words database 92 is generally organized in two-characterprefix data tables similar to those found in the generic word list 88.As such, the new frequency object 104 is initially assigned an absolutefrequency value, but upon storage the absolute frequency value, if it isnot the maximum value within that data table, will be changed to includea normalized frequency value percentage normalized to whatever is themaximum frequency value within that data table.

It is noted that the layout of the characters 48 disposed on the keys 28in FIG. 1 is an exemplary character layout that would be employed wherethe intended primary language used on the handheld electronic device 4was, for instance, English. Other layouts involving these characters 48and/or other characters can be used depending upon the intended primarylanguage and any language bias in the makeup of the language objects100.

While specific embodiments of the disclosed and claimed concept havebeen described in detail, it will be appreciated by those skilled in theart that various modifications and alternatives to those details couldbe developed in light of the overall teachings of the disclosure.Accordingly, the particular arrangements disclosed are meant to beillustrative only and not limiting as to the scope of the disclosed andclaimed concept which is to be given the full breadth of the claimsappended and any and all equivalents thereof.

1. A method of enabling input into a handheld electronic device comprising an input apparatus and a processor apparatus that comprises a memory, the input apparatus comprising a plurality of input members, at least some of the input members each having a number of linguistic elements assigned thereto, the memory having stored therein a plurality of language objects and a number of word frames, at least some of the language objects each comprising a number of the linguistic elements, the method comprising: detecting an entry on a handheld electronic device of a new language object not already stored in the memory, the new language object comprising a plurality of linguistic elements; determining with the processor apparatus that the at least a portion of the plurality of linguistic elements of the new language object comprises a sequential plurality of linguistic elements that are each assigned to a given input member; associating the new language object with a word frame, wherein the word frame is associated with a language object having a number of linguistic elements that is different from a number of linguistic elements of the new language object, and wherein the word frame comprises at least some of the plurality of linguistic elements of the new language object and a representation of the given input member in place of the sequential plurality of linguistic elements that are each assigned to the given input member.
 2. The method of claim 1, further comprising: making a determination that no word frame already exists that comprises at least some of the plurality of linguistic elements of the new language object and a representation of the given input member in place of the sequential plurality of linguistic elements that are each assigned to the given input member; and responsive to said making a determination, generating a new word frame comprising at least some of the plurality of linguistic elements of the new language object and a representation of the given input member in place of the sequential plurality of linguistic elements that are each assigned to the given input member.
 3. The method of claim 2, further comprising scanning the plurality of language objects to identify additional language objects that comprise said at least some of the plurality of linguistic elements of the new language object, and that further comprise a linguistic element assigned to the given input member.
 4. The method of claim 1, further comprising detecting as said entry one of: a number of input member actuations, and a quantity of received text.
 5. A handheld electronic device comprising: an input apparatus comprising a plurality of input members, at least some of the input members each having a number of linguistic elements assigned thereto; a processor apparatus comprising a processor and a memory, the memory having stored therein a plurality of language objects and a number of word frames; at least some of the language objects each comprising a number of the linguistic elements; and the memory having stored therein a number of routines which, when executed on the processor, cause the handheld electronic device to be adapted to perform operations comprising: detecting an entry of a new language object not already stored in the memory, the new language object comprising a plurality of linguistic elements; determining that the at least a portion of the plurality of linguistic elements of the new language object comprises a sequential plurality of linguistic elements that are each assigned to a given input member; associating the new language object with a word frame, wherein the word frame is associated with a language object having a number of linguistic elements that is different from a number of linguistic elements of the new language object, and wherein the word frame comprises at least some of the plurality of linguistic elements of the new language object and a representation of the given input member in place of the sequential plurality of linguistic elements that are each assigned to the given input member.
 6. The handheld electronic device of claim 5 wherein the operations further comprise: making a determination that no word frame already exists that comprises at least some of the plurality of linguistic elements of the new language object and a representation of the given input member in place of the sequential plurality of linguistic elements that are each assigned to the given input member; and responsive to said making a determination, generating a new word frame comprising at least some of the plurality of linguistic elements of the new language object and a representation of the given input member in place of the sequential plurality of linguistic elements that are each assigned to the given input member.
 7. The handheld electronic device of claim 6 wherein the operations further comprise scanning the plurality of language objects to identify additional language objects that comprise said at least some of the plurality of linguistic elements of the new language object, and that further comprise a linguistic element assigned to the given input member.
 8. The handheld electronic device of claim 5 wherein the operations further comprise detecting as said entry one of: a number of input member actuations, and a quantity of received text. 